The degree of tissue differentiation and prognostic significance of lung adenocarcinoma based on CT imaging features

WAN Guangyi; KONG Jiejun; ZHANG Lu

doi:10.19732/j.cnki.2096-6210.2024.04.008

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The degree of tissue differentiation and prognostic significance of lung adenocarcinoma based on CT imaging features

更新时间：2025-12-15

- The degree of tissue differentiation and prognostic significance of lung adenocarcinoma based on CT imaging features
- Vol. 33, Issue 4, Pages: 388-394(2024)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2024.04.008
  CLC：
- Published Online：12 September 2024，
  
  Published：12 September 2024
- 稿件说明：
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万光艺,孔杰俊,张璐. 基于CT影像学特征的肺腺癌组织分化程度分析及患者预后预测价值[J]. 肿瘤影像学, 2024, 33(4): 388-394 https://doi.

org/10.19732/j.cnki.2096-6210.2024.04.008
万光艺,孔杰俊,张璐. 基于CT影像学特征的肺腺癌组织分化程度分析及患者预后预测价值[J]. 肿瘤影像学, 2024, 33(4): 388-394 https://doi. DOI： 10.19732/j.cnki.2096-6210.2024.04.008.

org/10.19732/j.cnki.2096-6210.2024.04.008 DOI：

摘要

目的：

评估国际肺癌研究协会（International Association for the Study of Lung Cancer，IASLC）分级系统和传统计算机体层成像（computed tomography，CT）影像学特征之间的联系，并构建基于CT影像学特征的预后分层模型。

方

法：

回顾并分析2019年1月—2022年5月南京市胸科医院收治的102例原发性病理（p）Ⅰ期（T1N0M0或T2aN0M0）肺腺癌（lung adenocarcinoma，LUAD）患者的病历。根据2020年IASLC分级系统对患者进行分级，比较了不同IASLC组织学分级之间以及复发组和未复发组之间的临床病理和影像学特征。Logistic回归分析用于确定IASLC分级相关的CT征象，并通过多变量Cox回归模型确定患者无病生存期（disease-free survival，DFS）的影响因素。

结果：

102例LUAD患者分为1级15例（14.7%），2级63例（61.8%）和3级24例（23.5%）。在30.4个月随访期间，16例（15.7%）患者复发。较高的CTR（OR=2.152，95% CI 1.530~3.264，

=0.005）和较高的CT值（OR=3.730，95% CI 2.841~6.353，

=0.001）是较高组织学分级的独立危险因素。联合上述2个独立因素预测IASLC 3级的曲线下面积（area under curve，AUC）为0.912（95% CI0.877~0.937；

＜0.001），与单独使用平均CT值或实变肿瘤比率（consolidation tumor ratio，CTR）的AUC差异无统计学意义。多变量Cox回归分析显示，年龄（HR=1.05，95% CI 1.02~1.09，

=0.003）、CTR（HR=2.81，95% CI 1.16~6.77，

=0.022）、CT值（HR=2.49，95% CI 1.19~5.25，

=0.016）、毛刺征（HR=5.96，95% CI 2.30~15.43，

＜0.001）和组织学分级（HR=4.31，95% CI 2.28~8.14，

＜0.001）是DFS的独立危险因素。

结论：

较大的CTR以及较高的平均CT值是较高IASLC组织学分级的独立预测因子。CTR（截断值＜0.25

和≥0.75）和平均CT值（截断值＜-410 HU和≥-210 HU）可用作IASLC分级系统的术前替代物。

Abstract

Objective:

To evaluate the relationship between International Association for the Study of Lung Cancer (IASLC) classification system and traditional computed tomography (CT) imaging features

and to construct a hierarchical prognosis model based on CT imaging features.

Methods:

The study retrospectively analyzed the medical records of 102 consecutive patients with primary pathological (p) stage I (T1N0M0 or T2aN0M0) LUAD in Nanjing Chest Hospital from January 2019 to May 2022. According to the 2020 IASLC grading system

patients were classified and the clinical pathological and imaging features were compared between different IASLC histological grades

as well as between recurrent and non recurrent groups. Logistic regression analysis was used to determine CT signs related to IASLC grading

and to determine influencing factors for disease-free survival (DFS) of patients through a multivariate Cox regression model.

Results:

A total of 102 patients with LUAD were divided into grade 1 (15 cases

14.7%)

grade 2 (63 cases

61.8%) and grade 3 (24 cases

23.5%). During the 30.4 months follow-up

16 patients (15.7%) relapsed. Higher CTR (OR=2.152

95% CI 1.530-3.264

=0.005) and higher CT value (OR=3.730

95% CI 2.841-6.353

=0.001) were independent risks of higher histological grade. The AUC value for conjoining the above two independentfactors to predict grade 3 was 0.912 (95% CI 0.877-0.937

＜0.001)

and it was not significantly different from the AUC for using the mean CT value or CTR alone. Multivariate Cox regression analysis showed age (HR=1.05

95% CI 1.02-1.09

=0.003)

CTR (HR=2.81

95% CI 1.16-6.77

=0.022)

CT value (HR=2.49

95% CI 2.30-15.43

0.001) and histological grade (HR=4.31

95% CI 2.28-8.14

＜0.001) were independent risk factors for DFS.

Conclusion:

Larger CTR and higher average CT value are independent predictors of higher IASLC histological grade. CTR (truncation values＜0.25 and ≥0.75) and average CT values (truncation values＜-410 HU and ≥-210 HU) can be used as preoperative substitutes for IASLC grading system.

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